Far-field earthquake triggering mechanisms represent a critical frontier in earthquake prediction research. To explore the spatiotemporal relationship between strong seismic activity in Japan and moderate-to-strong earthquakes in northeast China, this study develops a data-driven prediction model. Based on the USGS earthquake catalog from 1980 to 2024, and using M≥6.0 earthquakes in Japan as potential triggers, the model predicts the probability of earthquakes with M≥4 occurring in northeast China within the next 60 days. A daily-resolution time series dataset is constructed, incorporating 32-dimensional features covering earthquake statistics, spatial distribution, energy release, and aftershock sequences. Deep learning models including long short-term memory (LSTM) neural networks, attention mechanism-enhanced LSTM (Attention-LSTM), and Transformer are systematically compared against traditional machine learning methods such as logistic regression and random forest. Results indicate that the Attention-LSTM model performs optimally, achieving an F1 score of 0.915 and an AUC value of 0.661, and shows significant advantages in Molchan error diagram analysis, a method specific to earthquake prediction evaluation. The model enables both binary classification prediction and generation of spatial probability distribution maps at 1°×1° grid resolution. This study demonstrates the potential of deep learning for revealing cross-regional far-field earthquake triggering mechanisms and provides new insights for short-to-medium-term regional seismic hazard assessment.